Learn to unlock the power of data engineering at scale for analytics success
Data engineers rely on Apache Spark to process large data volumes at incredible speeds. Its ability to accelerate the ingestion, exploration, and cataloging of data types from multiple sources lets teams quickly build batch or streaming pipelines with relative ease.
But for all of its processing prowess, Spark still requires a significant amount of manual work and struggles with ineffective data engineering in production at scale. Read this ebook to:
Understand how Cloudera Data Engineering (CDE) can enable you to leverage Spark for processing large data volumes
Review strategies to optimize enterprise data from ingest to insight at scale
Learn to harness the power of Spark without impeding on data engineering scalability
Recognize ways to drive enterprise analytics and machine learning success